Systems and methods determining plant population and weed growth statistics from airborne measurements in row crops

ABSTRACT

This disclosure describes a system and a method for determining statistics of plant populations based on overhead optical measurements. The system may include one or more hardware processors configured by machine-readable instructions to receive output signals provided by one or more remote sensing devices mounted to an overhead platform. The output signals may convey information related to one or more images of a land area where crops are grown. The one or more hardware processors may be configured by machine-readable instructions to distinguish vegetation from background clutter; segregate image regions corresponding to the vegetation from image regions corresponding to the background clutter; and determine a plant count per unit area.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/268,370 entitled SYSTEMS AND METHODS FOR DETERMINING STATISTICS OFPLANT POPULATIONS BASED ON OVERHEAD OPTICAL MEASUREMENTS, filed Sep. 16,2016, which claims the benefit of U.S. Provisional Patent ApplicationNo. 62/220,596 entitled SYSTEMS AND METHODS DETERMINING PLANT POPULATIONAND WEED GROWTH STATISTICS FROM AIRBORNE MEASUREMENTS IN ROW CROPS,filed Sep. 18, 2015, the contents of which are each herein incorporatedby reference in their entirety.

FIELD OF THE DISCLOSURE

This disclosure relates to systems and methods for determiningstatistics of plant populations based on overhead optical measurements.

BACKGROUND

Farming practices may become more efficient by informing growers withmore accurate and thorough information on the status of their crops. Forexample, timely and accurate knowledge of the emergent plant density andsize distribution and their spatial variances across the field mayenable growers and agronomists to a) determine more accurately how theemergent crop population differs from the planned population and wherereplanting may be necessary; b) detect poor germination areas which canthen be investigated for bad seed, bad soil, or other disadvantageousconditions; c) detect malfunctioning planting implements for correctiveaction; d) more accurately and selectively apply inputs such asfertilizers, fungicides, herbicides, pesticides, and other inputs; e)more thoroughly understand how combinations of seed types, plantingdensities, soil chemistries, irrigation, fertilizers, chemicals, etc.contribute to crop populations which optimize production yields.

SUMMARY

Current solutions for estimating plant population statistics (or “standcount”) may include humans manually counting individual plants atmultiple, yet sparse, locations across a field. The area surveyed bythis method may be less than 1% of the total field area. An estimate forthe entire field may be determined through interpolation whichconsequently may lead to large errors as entire portions of the fieldmay not be surveyed and may include unplanted, misplanted, or damagedareas.

More recently, airborne observations have been employed. While airbornemethods may benefit by replacing the sparse sampling and interpolationlimitations of manual counting with 100% coverage, they have beengreatly limited by their ability to a) resolve individual plants; b)discriminate individual plants of the crop species from other plants(weeds) or detritus in the field for automated counting; and c)accurately determine the area of the measured region due to inaccuraciesin aircraft altitude and ground elevation measurements. Theselimitations have led to very large errors in population statistics.

Exemplary implementations of the present disclosure may employ a remotesensing system (e.g. multispectral, hyperspectral, panchromatic, and/orother sensors) mounted to an airborne or other overhead platform andautomated computer vision techniques to detect, resolve, anddiscriminate crop plants for counting and sizing over large areas ofagricultural fields.

Accordingly, one aspect of the disclosure relates to a system configuredfor determining statistics of plant populations based on overheadoptical measurements. The system may comprise one or more hardwareprocessors configured by machine-readable instructions to receive outputsignals provided by one or more remote sensing devices mounted to anoverhead platform. The output signals may convey information related toone or more images of a land area where crops are grown. The one or moreimages may be spatially resolved. The output signals may include one ormore channels corresponding to one or more spectral ranges. The one ormore hardware processors may be configured by machine-readableinstructions to distinguish vegetation from background based on the oneor more channels. The vegetation may include one or both of a croppopulation and a non-crop population. The background clutter may includeone or more of soil, standing water, man-made materials, deadvegetation, or other detritus. The one or more hardware processors maybe configured by machine-readable instructions to segregate imageregions corresponding to the vegetation from image regions correspondingto the background clutter. The one or more hardware processors may beconfigured by machine-readable instructions to determine a plant countper unit area.

Another aspect of the disclosure relates to a method for determiningstatistics of plant populations based on overhead optical measurements.The method may be performed by one or more hardware processorsconfigured by machine-readable instructions. The method may includereceiving output signals provided by one or more remote sensing devicesmounted to an overhead platform. The output signals may conveyinformation related to one or more images of a land area where crops aregrown. The one or more images may be spatially resolved. The outputsignals may include one or more channels corresponding to one or morespectral ranges. The method may include distinguishing vegetation frombackground clutter based on the one or more channels. The vegetation mayinclude one or both of a crop population and a non-crop population. Thebackground clutter may include one or more of soil, standing water,man-made materials, dead vegetation, or other detritus. The method mayinclude segregating image regions corresponding to the vegetation fromimage regions corresponding to the background clutter. The method mayinclude determining a plant count per unit area.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system configured for determining statistics ofplant populations based on overhead optical measurements, in accordancewith one or more implementations.

FIG. 2 illustrates spectral images obtained from an airborne platform,in accordance with one or more implementations.

FIG. 3 illustrates segregation of vegetation from background clutter, inaccordance with one or more implementations.

FIG. 4 illustrates segregation of large rafts of non-crop populationfrom the crop population, in accordance with one or moreimplementations.

FIG. 5 illustrates detection and characterization of crop rows, inaccordance with one or more implementations.

FIG. 6 illustrates segregation of vegetation growing within rows fromvegetation growing outside of rows, in accordance with one or moreimplementations.

FIG. 7 illustrates complete segregation of crop population from non-croppopulation and background clutter, in accordance with one or moreimplementations.

FIG. 8 illustrates a map of plant center positions, in accordance withone or more implementations.

FIG. 9 illustrates a method for determining statistics of plantpopulations based on overhead optical measurements, in accordance withone or more implementations.

FIG. 10 illustrates process steps performed by the system of FIG. 1, inaccordance with one or more implementations.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 10 configured for determining statistics ofplant populations based on overhead optical measurements, in accordancewith one or more implementations. In some implementations, system 10 mayinclude one or more remote sensing devices 24. In some implementations,system 10 may include one or more server 12. Server(s) 12 may beconfigured to communicate with one or more client computing platforms 18and/or one or more remote sensing devices 24 according to aclient/server architecture. The users may access system 10 via a userinterface 20 of client computing platform(s) 18.

The one or more remote sensing devices 24 may be mounted to an overheadplatform. In some implementations, the overhead platform may include oneor more of an aircraft, a spacecraft, an unmanned aerial vehicle, adrone, a tower, a vehicle, a tethered balloon, farming infrastructuresuch as center pivot irrigation systems or other infrastructure, and/orother overhead platforms. In some implementations, the one or moreremote sensing devices 24 may be configured to provide output signals.The output signals may convey information related to one or more imagesof a land area where crops are grown. In some implementations, the oneor more images may include one or more spectral measurements. Forexample, the one or more images may include one or more of a colormeasurement, a multi-spectral measurement, a hyperspectral measurement,and/or other spectral measurements of a land area where crops are grown.In some implementations, the one or more remote sensing devices 24 mayrecord two-dimensional images of the land area where crops are grownformed on a single or multiple focal plane arrays. For example, a coloror multispectral measurement may be formed through multiple spectralfilters applied to individual pixels in a single focal plane array, orthrough spectral filters applied to entire focal plane arrays in amultiple focal plane array configuration.

In some implementations, the one or more images may be of sufficientspatial resolution to detect individual plants within the croppopulation. In some implementations, the one or more images may be ofsufficient spectral resolution to resolve spectral differences betweengrowing vegetation and background clutter. In some implementations, themeasurements may be of sufficient resolution such that the groundresolved distance (GRD) is smaller than a characteristic dimension ofone or more target plants in the land area.

In some implementations, the one or more remote sensing devices 24 mayprovide output signals conveying information related to one or more of atime stamp, a position (e.g., latitude, longitude, and/or altitude), anattitude (e.g., roll, pitch, and/or yaw/heading), a spectral measurementof solar irradiance, calibration information specific to the device,and/or other information corresponding to individual ones of the one ormore images. In some implementations, calibration may include adjustingthe one or more images for sunlight conditions, systemic errors, orpositioning the image onto the earth's surface for output mapping. Insome implementations, the one or more remote sensing devices 24 mayprovide output signals conveying information related to one or moreenvironmental parameters, time stamp, the position, the attitude, and/orother information corresponding to individual ones of the one or moreimages. For example, the one or more environmental parameters mayinclude spectral measurements of downwelling solar illuminance,temperature, relative humidity, and/or other weather or environmentalconditions. In some implementations, output signals conveyinginformation related to one or more environmental parameters, time stamp,the position, the attitude, and/or other information may be utilized tocalibrate the one or more spectral images. In some implementations, theoutput signals may be synchronous to the one or more images. Forexample, each image may include the output signals as metadata whosetime of validity corresponds to the image.

By way of a non-limiting example, FIG. 2 illustrates spectral imagesobtained from an airborne platform, in accordance with one or moreimplementations. As shown on FIG. 2, an airborne platform 210 having oneor more remote sensing devices may provide one or more images 220 of aland area 230 where crops are grown.

Returning to FIG. 1, the server(s) 12 and/or client computingplatform(s) 18 may be configured to execute machine-readableinstructions 26. The machine-readable instructions 26 may include one ormore of a communications component 28, an image revision component 30, acontrast adjustment component 32, a background clutter segregationcomponent 34, a crop segregation component 36, a crop row attributes 38,a crop density determination component 40, a presentation component 42,and/or other components.

Machine-readable instructions 26 may facilitate determining statisticsof plant populations based on overhead optical measurements. In someimplementations, communications component 28 may receive output signalsprovided by one or more remote sensing devices mounted to an overheadplatform. In some implementations, the output signals may include one ormore spectral images, metadata related to the one or more spectralimages, and/or other information. The output signals may conveyinformation related to one or more images of a land area where crops aregrown. In some implementations, the one or more images may be spatiallyresolved and spectrally resolved. In some implementations, spatiallyresolved images may include one or more images corresponding to cropplants, non-crop plants, a land area, and/or other locations. In someimplementations, the one or more images may include individual pixelscorresponding to a spectral range. In some implementations, theindividual pixels may include intensity values corresponding to thespectral range. For example, the one or more remote sensing devices mayinclude a first camera having a red filter thereon and a second camerahaving a near infrared filter thereon. An Image captured by the firstcamera may include pixel values indicating intensity in the red spectralrange and an image captured by the second camera may include pixelvalues indicating intensity in the near infrared spectral range. In someimplementations, the output signals may include one or more channels. Insome implementations, multiple channels may be part of a single remotesensing device. In some implementations, multiple channels may be partof multiple remote sensing devices. In some implementations, each imagemay be created by a channel. In some implementations, each image createdby a channel may be both spatially and spectrally resolved. In someimplementations, individual channels may have a similar spatialresolution. In some implementations, different spectral ranges may beresolved in each channel. In some implementations, a stack of images maybe based on the one or more channels.

In some implementations, image revisions component 30 may be configuredto correct and/or revise systematic and environmental errors common tospectral imaging systems as described, for example in U.S. patentapplication Ser. No. 14/480,565, filed Sep. 8, 2014, and entitled“SYSTEM AND METHOD FOR CALIBRATING IMAGING MEASUREMENTS TAKEN FROMAERIAL VEHICLES” which is hereby incorporated into this disclosure byreference in its entirety. In some implementations, image revisionscomponent 30 may revise one or more intensity non-uniformities of theone or more images. The one or more intensity non-uniformities may beresults from characteristics of one or more collection optics. In someimplementations, image revisions component 30 may revise one or morespatial distortions of the one or more images. The one or more spatialdistortions may be due to one or more characteristics of the collectionoptics. In some implementations, image revisions component 30 may reviseone or more variations in intensity that result from changes in solarirradiance of the one or more images. For example, image revisionscomponent 30 may utilize one or more of a collocated solar spectrometer,a solar intensity measurement, a reflectance standard, and/or othercalibration device or technique to revise the one or more images forvariations in solar irradiance.

In some implementations, image revisions component 30 may be configuredto register one or more pixels from the one or more channels to a commonpixel space. The first channel may correspond to a first spectral rangeand the second channel may correspond to a second spectral range. Forexample, one or more pixels of the first channel and the second channelmay be registered to a common pixel space such that the correspondingpixels of each channel provide measurements of a common area of thetarget scene. In some implementations, cross-channel registration mayinclude two-dimensional cross-correlation and/or other techniques todetermine the translation, rotation, scaling, and/or warping to beapplied to each channel such that one or more pixels from the one ormore channels are registered to a common pixel space.

In some implementations, contrast adjustment component 32 may beconfigured to distinguish vegetation from background clutter based onthe one or more channels. In some implementations, the vegetation mayinclude one or both of a crop population and a non-crop population. Insome implementations, the background clutter may include one or more ofsoil, standing water, pavement, man-made materials, dead vegetation,other detritus, and/or other background clutter. In someimplementations, contrast adjustment component 32 may numericallycombine the one or more channels such that a contrast between thevegetation and the background clutter is increased. In someimplementations, contrast adjustment component 32 may combine the one ormore channels in a ratio or other index such that a contrast between thevegetation and the background clutter is increased. In someimplementations, the combination may include a Difference VegetationIndex (Difference VI), a Ratio Vegetation Index (Ratio VI), aChlorophyll Index, a Normalized Difference Vegetation Index (NDVI), aPhotochemical Reflectance Index (PRI), and/or other combinations ofchannels. In some implementations, contrast adjustment component 32 mayamplify the contrast of one or more high spatial frequency componentscorresponding to the combination. For example, a two-dimensionalbandpass filter may be used to suppress signals of spatial frequencieslower than the crop plants or an edge sharpening filter may be used toincrease the contrast of plant and non-plant boundaries in the images.By way of a non-limiting example, FIG. 3 illustrates segregation ofvegetation from background clutter, in accordance with one or moreimplementations. In FIG. 3, a false color image 310 may be convertedinto a high contrast image 320 which segregates growing vegetation 330from background clutter 340.

Returning to FIG. 1, background clutter segregation component 34 may beconfigured to segregate image regions corresponding to the vegetationfrom image regions corresponding to the background clutter. In someimplementations, background clutter segregation component 34 may beconfigured to utilize differing spectral reflectance combinations acrossmultiple wavelength bands to segregate target types. In someimplementations, background clutter segregation component 34 may beconfigured to determine an initial threshold value for the combination.The initial threshold value may be selected to segregate pixelscontaining vegetation signals from pixels containing background clutter.In some implementations, background clutter segregation component 34 maycompare each pixel value in the combination to the threshold value. Insome implementations, background clutter segregation component 34 maygroup adjacent pixels that compare to the threshold value correspondingto the vegetation into “blobs.” In some implementations, backgroundclutter segregation component 34 may count a total number of independentblobs with a “blob counting” algorithm and store the count with thevalue of the threshold.

In some implementations, background clutter segregation component 34 maybe configured to adjust the value of the combination threshold to a newvalue. In some implementations, the combination threshold valueadjustment may be repeated for a range of values such that arelationship may be established between the threshold and the number ofblobs detected. In some implementations, background clutter segregationcomponent 34 may establish a relationship between the ratio thresholdand the number of vegetation “blobs” in the ratio image. In someimplementations, background clutter segregation component 34 may beconfigured to determine a threshold value where detection count plateaussuch that the blob count is most stable to changes in threshold. In someimplementations, background clutter segregation component 34 may beconfigured to provide a two-dimensional matrix where each entry is abinary value indicating the presence (or absence) of vegetation withinthe corresponding pixel.

In some implementations, crop segregation component 36 may be configuredto segregate image regions corresponding to the crop population fromimage regions corresponding to the non-crop population in the imageregions corresponding to the vegetation. In some implementations, cropsegregation component 36 may perform an erosion operation on the binarymatrix to segregate individual plants which may be grouped together intosingle blobs. In some implementations, crop segregation component 36 maydetermine a characteristic size of the crop population based on astatistical distribution of the vegetation size. In someimplementations, crop segregation component 36 may segregate one or morecontiguous groups of vegetation pixels having a size substantiallygreater than the characteristic size of the crop population. Forexample, crop segregation component 36 may be configured to classify andsegregate large rafts of weeds from the crop population by identifyingblob sizes that are larger and statistically separable from the mainpopulation of crop population. In some implementations, crop segregationcomponent 36 may be configured to remove the large rafts of weeds(non-crop population) from the binary matrix of vegetation detections.By way of a non-limiting example, FIG. 4 illustrates segregation oflarge rafts of non-crop population from the crop population, inaccordance with one or more implementations. As depicted in FIG. 4,successive erosion operations 1-4 are performed on the one or moreimages such that only the large non-crop population areas 410 remain.

Returning to FIG. 1, crop row attributes component 38 may be configuredto perform a two-dimensional Fast Fourier Transform (FFT) on the one ormore images or on the numerical combination of images from the one ormore channels as determined previously to determine the spatialfrequencies, orientation, and curvature of peak energy with respect tothe one or more images. In some implementations, crop row attributescomponent 38 may identify two local maxima of peak energy correspondingto crop row spacing (the lowest frequency local maxima) and individualplant spacing along rows (the highest frequency local maxima). AnInverse Fast Fourier Transform (IFFT) of the low frequency local maximamay provide the spatial separation of crop rows and their orientationrelative to the one or more images.

In some implementations, crop row attributes component 38 may perform aHough transform to provide the location of each row in the one or moreimages along with individual row orientation, spacing, and curvature. Byway of a non-limiting example, FIG. 5 illustrates detection andcharacterization of crop rows, in accordance with one or moreimplementations. In FIG. 5, crop rows 510, crop row spacing 520 in pixelcoordinates, and crop row orientation 530 relative to the one or moreremote sensing devices have been determined. In some implementations,crop row attributes component 38 may determine a spacing of one or morecrop rows in pixels. In some implementations, crop row attributescomponent 38 may determine the pixel's Ground Sample Dimension usingexternally provided (e.g., by external resources 16) row spacing and therow spacing in pixels.

Returning to FIG. 1, crop row attributes component 38 may be configuredto provide a mask to segregate vegetation belonging to the croppopulation from vegetation belonging to the non-crop population usingthe previously determined crop row information. In some implementations,crop row attributes component 38 may be configured to characterize areference spectral signature of vegetation within the one or more croprows. In some implementations, crop row attributes component 38 may beconfigured to accept as input a prescribed reference spectral signaturefrom an external resource 16 or may calculate a reference spectralsignature by user selection of a region of interest. In someimplementations, crop row attributes component 38 may be configured tostatistically compare the spectral signature of each pixel to thereference spectral signature. In some implementations, the statisticalproximity of each pixel's spectral signature to the reference spectralsignature may be used to classify the pixel as belonging to the croppopulation class or another class. For example, individual plantdetections that were classified in the crop class but have statisticallydifferent spectral signatures from the reference spectral signature maybe reclassified as non-crop plants. Similarly, the reference spectralsignature may be used to classify other plant or non-plant pixels.

In some implementations, a user may make a selection of a region ofinterest in the one or more images. In some implementations, crop rowattributes component 38 may determine a spectral signature correspondingto the region of interest. In some implementations, crop row attributescomponent 38 may determine one or more additional regions and/or pixelsin the one or more images having a statistically similar spectralsignature. In some implementations, crop row attributes component 38 mayclassify the one or more additional regions and/or pixels as belongingto the crop population class, the non-crop population class, or anotherclass.

By way of a non-limiting example, FIG. 6 illustrates segregation ofvegetation growing within rows from vegetation growing outside of rows,in accordance with one or more implementations. As depicted in FIG. 6, acrop row mask 610 is represented as a series of thick lines or curvedlines, each fully encompassing one crop row. In FIG. 6, once thelocation and orientation of the crop rows have been determined, mask 610is applied to segregate vegetation growing within the rows (e.g., thecrop population) from vegetation 620 growing outside of the rows (e.g.,non-crop population). In some implementations, the width of the crop rowmask 610 may be determined by using a priori information about the cropand/or by dynamically determining the crop width from the image based onthe statistical crop size.

In some implementations, crop row attributes component 38 may beconfigured to classify groups of vegetation pixels as belonging to thecrop population if they are positioned statistically within the croprows. In some implementations, crop row attributes component 38 may beconfigured to classify groups of vegetation pixels as belonging to thenon-crop population if they are positioned statistically outside of thecrop rows.

In some implementations, crop row attributes component 38 may beconfigured to determine a new and dynamic threshold level to improve thesegregation of the crop population from the background noise by creatinga histogram of pixel values only within the masked crop rows. Thehistogram may be utilized to determine the correct threshold to separatethe plants from the background clutter. In some implementations,background clutter may include one or more of soil, shadows, deadvegetation, weeds, standing water, farming equipment, and/or otherbackground clutter. In some implementations, the newly determinedthreshold may be applied to the whole image. By way of a non-limitingexample, FIG. 7 illustrates complete segregation of crop population 720from non-crop population 730 and background clutter 710, in accordancewith one or more implementations. FIG. 7 depicts the segregation of croppopulation 720 from non-crop population 730 resulting from theutilization of a histogram and determination of a new threshold value.

Returning to FIG. 1, crop density determination component 40 maydetermine a crop density corresponding to the crop population and anon-crop density corresponding to the non-crop population. In order toaccurately determine vegetation density per unit area, the area overwhich the vegetation count was conducted may need to be accuratelydetermined. While the optical characteristics (i.e. field of view) ofthe one or more remote sensing devices may be accurately known, thealtitude of the one or more remote sensing devices above the groundlevel may be more difficult to determine. Accordingly, crop densitydetermination component 40 may convert the determined row spacing frompixels to a linear spatial dimension (e.g., centimeters). In someimplementations, crop density determination component 40 may determinean area of land portrayed by the one or more images using the convertedrow spacing. In some implementations, crop density determinationcomponent 40 may determine a first count corresponding to the croppopulation and a second count corresponding to the non-crop populationper unit area for one or more of the images. In some implementations,crop density determination component 40 may determine a crop countand/or non-crop count per unit area for one or more sub-regions of theone or more images. In some implementations, crop density determinationcomponent 40 may determine an area of land portrayed by the one or moreimages using the number of pixels in the image and the pixel's GroundSample Dimension.

In some implementations, crop density determination component 40 mayutilize blob detection techniques and/or other algorithms to identifyand count each of the crop plants within the crop row mask. In someimplementations, crop density determination component 40 may determine acentroid position of each blob. In some implementations, crop densitydetermination component 40 may provide a list of plant center positioncoordinates. By way of a non-limiting example, FIG. 8 illustrates a mapof plant center positions, in accordance with one or moreimplementations. FIG. 8 depicts plant centers 810 that are locatedwithin each image to determine spacing and count per area.

In some implementations, crop density determination component 40 maydetermine a pixel distance between each center position using the centerposition coordinates. In some implementations, crop densitydetermination component 40 may provide a histogram of center to centerspacing that may yield a strong peak at the nominal plant spacing. Insome implementations, crop density determination component 40 maycombine the nominal in-row plant spacing with row-to-row spacing togenerate nominal planting density (e.g., plants per acre). In someimplementations, crop density determination component 40 may receiveuser inputs regarding plant spacing and row spacing. In someimplementations, crop density determination component 40 may utilize thereceived user inputs to refine the results of the planting statistics.

In some implementations, crop density determination component 40 maydetermine a refined plant count through analysis of the length of eachblob along the plant row, and/or the spacing between plant centers. Insome implementations, crop density determination component 40 mayutilize statistics determined through the analysis to account for twoplants that have grown together and appear as a single plant or singleplants whose leaf structure causes them to appear as two or more plants.

In some implementations, crop density determination component 40 maydetermine statistics of the crop population including one or more ofplant count per unit area, plant size, plant health, and/or otherstatistics. In some implementations, crop density determinationcomponent 40 may determine the crop density by dividing the first countby the determined area of the one or more images. In someimplementations, crop density determination component 40 may determinethe non-crop density by dividing the second count by the determined areaof the one or more images.

In some implementations, crop density determination component 40 maydetermine plant size statistics by determining a number of contiguouspixels which constitute individual plants. In some implementations, cropdensity determination component 40 may determine plant healthcharacteristics using one or more of spectral combination methods. Forexample, combinations of spectral reflectance values may be used toinfer conditions of plant health. Such combinations may includeDifference Vegetation Index (Difference VI), a Ratio Vegetation Index(Ratio VI), a Chlorophyll Index, a Normalized Difference VegetationIndex (NDVI), a Photochemical Reflectance Index (PRI), and/or othercombinations of channels.

In some implementations, operations corresponding to one or more ofcommunications component 28, image revision component 30, contrastadjustment component 32, background clutter segregation component 34,crop segregation component 36, crop row attributes 38, crop densitydetermination component 40, and/or other components may be repeated formultiple overlapping spectral images that cover large farming areas.

In some implementations, presentation component 42 may be configured toeffectuate presentation of one or both of a map corresponding to thecrop density or a map corresponding to the non-crop density. In someimplementations, presentation component 42 may be configured tointerpolate and/or resample results for the multiple spectral imagesonto a common grid spacing for the entire survey area. In someimplementations, presentation component 42 may be configured to formatthe map corresponding to the crop density and/or the map correspondingto the non-crop density into multiple file formats for ease ofdissemination, review, and further analysis in other downstream dataproducts.

In some implementations, server(s) 12, client computing platform(s) 18,and/or external resources 16 may be operatively linked via one or moreelectronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 12, clientcomputing platform(s) 18, and/or external resources 16 may beoperatively linked via some other communication media.

A given client computing platform 18 may include one or more processorsconfigured to execute machine-readable instructions. Themachine-readable instructions may be configured to automatically, orthrough an expert or user associated with the given client computingplatform 18 to interface with system 10 and/or external resources 16,and/or provide other functionality attributed herein to client computingplatform(s) 18. In some implementations, the one or more processors maybe configured to execute machine-readable instruction components 28, 30,32, 34, 36, 38, 40, 42, and/or other machine-readable instructioncomponents. By way of non-limiting example, the given client computingplatform 18 may include one or more of a desktop computer, a laptopcomputer, a handheld computer, a tablet computing platform, a NetBook, aSmartphone, a gaming console, and/or other computing platforms.

In some implementations, the one or more remote sensing devices 24 mayinclude one or more processors configured to execute machine-readableinstructions. The machine-readable instructions may be configured toautomatically, or through an expert or user associated with the one ormore remote sensing devices 24 to interface with system 10 and/orexternal resources 16, and/or provide other functionality attributedherein to the one or more remote sensing devices 24. In someimplementations, the one or more processors may be configured to executemachine-readable instruction components 28, 30, 32, 34, 36, 38, 40, 42,and/or other machine-readable instruction components. In someimplementations, the one or more remote sensing devices 24 may includeprocessors 22 and electronic storage 14.

External resources 16 may include sources of information, hosts and/orproviders of digital media items outside of system 10, external entitiesparticipating with system 10, and/or other resources. In someimplementations, some or all of the functionality attributed herein toexternal resources 16 may be provided by resources included in system10.

Server(s) 12 may include electronic storage 14, one or more processors22, and/or other components. Server(s) 12 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 12 in FIG. 1is not intended to be limiting. Server(s) 12 may include a plurality ofhardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 12. Forexample, server(s) 12 may be implemented by a cloud of computingplatforms operating together as server(s) 12.

Electronic storage 14 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 14 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)12 and/or removable storage that is removably connectable to server(s)12 via, for example, a port (e.g., a USB port, a firewire port, etc.) ora drive (e.g., a disk drive, etc.). Electronic storage 14 may includeone or more of optically readable storage media (e.g., optical disks,etc.), magnetically readable storage media (e.g., magnetic tape,magnetic hard drive, floppy drive, etc.), electrical charge-basedstorage media (e.g., EEPROM, RAM, etc.), solid-state storage media(e.g., flash drive, etc.), and/or other electronically readable storagemedia. Electronic storage 14 may include one or more virtual storageresources (e.g., cloud storage, a virtual private network, and/or othervirtual storage resources). Electronic storage 14 may store softwarealgorithms, information determined by processor(s) 22, informationreceived from server(s) 12, information received from client computingplatform(s) 18, and/or other information that enables server(s) 12 tofunction as described herein.

Processor(s) 22 is configured to provide information processingcapabilities in server(s) 12. As such, processor(s) 22 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 22 is shown in FIG. 1 as asingle entity, this is for illustrative purposes only. In someimplementations, processor(s) 22 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 22 may represent processing functionality of aplurality of devices operating in coordination. The processor(s) 22 maybe configured to execute machine-readable instruction components 28, 30,32, 34, 36, 38, 40, 42, and/or other machine-readable instructioncomponents. The processor(s) 22 may be configured to executemachine-readable instruction components 28, 30, 32, 34, 36, 38, 40, 42,and/or other machine-readable instruction components by software;hardware; firmware; some combination of software, hardware, and/orfirmware; and/or other mechanisms for configuring processingcapabilities on processor(s) 22.

It should be appreciated that although machine-readable instructioncomponents 28, 30, 32, 34, 36, 38, 40, and 42 are illustrated in FIG. 1as being implemented within a single processing unit, in implementationsin which processor(s) 22 includes multiple processing units, one or moreof machine-readable instruction components 28, 30, 32, 34, 36, 38, 40,and/or 42 may be implemented remotely from the other components and/orsubcomponents. The description of the functionality provided by thedifferent machine-readable instruction components 28, 30, 32, 34, 36,38, 40, and/or 42 described herein is for illustrative purposes, and isnot intended to be limiting, as any of machine-readable instructioncomponents 28, 30, 32, 34, 36, 38, 40, and/or 42 may provide more orless functionality than is described. For example, one or more ofmachine-readable instruction components 28, 30, 32, 34, 36, 38, 40,and/or 42 may be eliminated, and some or all of its functionality may beprovided by other ones of machine-readable instruction components 28,30, 32, 34, 36, 38, 40, and/or 42. As another example, processor(s) 22may be configured to execute one or more additional machine-readableinstruction components that may perform some or all of the functionalityattributed below to one of machine-readable instruction components 28,30, 32, 34, 36, 38, 40, and/or 42.

FIG. 9 illustrates a method 900 for determining statistics of plantpopulations based on overhead optical measurements, in accordance withone or more implementations. The operations of method 900 presentedbelow are intended to be illustrative. In some implementations, method900 may be accomplished with one or more additional operations notdescribed, and/or without one or more of the operations discussed.Additionally, the order in which the operations of method 900 areillustrated in FIG. 9 and described below is not intended to belimiting.

In some implementations, method 900 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 900 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 900.

At an operation 905, output signals provided by one or more remotesensing devices mounted to an overhead platform may be received. In someimplementations, the output signals may convey information related toone or more images of a land area where crops are grown. In someimplementations, the one or more images may be spatially resolved andspectrally resolved. In some implementations, the output signals mayinclude one or more channels. In some implementations, the first channelmay correspond to a first spectral range and the second channel maycorrespond to a second spectral range. Operation 905 may be performed byone or more hardware processors configured to execute a machine-readableinstruction component that is the same as or similar to communicationscomponent 28 and image revisions component 30 (as described inconnection with FIG. 1), in accordance with one or more implementations.

At an operation 910, vegetation may be distinguished from backgroundclutter based on the one or more channels. In some implementations, thevegetation may include one or both of a crop population and a non-croppopulation. In some implementations, the background clutter may includeone or more of soil, standing water, man-made materials, deadvegetation, and/or other background clutter. Operation 910 may beperformed by one or more hardware processors configured to execute amachine-readable instruction component that is the same as or similar tocontrast adjustment component 32 (as described in connection with FIG.1), in accordance with one or more implementations.

At an operation 915, image regions corresponding to the vegetation maybe segregated from image regions corresponding to the backgroundclutter. Operation 915 may be performed by one or more hardwareprocessors configured to execute a machine-readable instructioncomponent that is the same as or similar to background cluttersegregation component 34 (as described in connection with FIG. 1), inaccordance with one or more implementations.

At an operation 920, image regions corresponding to the crop populationmay be segregated from image regions corresponding to the non-croppopulation in the image regions corresponding to the vegetation.Operation 920 may be performed by one or more hardware processorsconfigured to execute a machine-readable instruction component that isthe same as or similar to crop segregation component 36 (as described inconnection with FIG. 1), in accordance with one or more implementations.

At an operation 925, a crop density corresponding to the crop populationand a non-crop density corresponding to the non-crop population may bedetermined. Operation 925 may be performed by one or more hardwareprocessors configured to execute a machine-readable instructioncomponent that is the same as or similar to crop row attributescomponent 38 and crop density determination component 40 (as describedin connection with FIG. 1), in accordance with one or moreimplementations.

By way of a non-limiting example, FIG. 10 illustrates process steps 1000performed by the system of FIG. 1, in accordance with one or moreimplementations. As depicted in FIG. 10, system 10 may be configured toperform process steps 1002-1006 with the one or more remote sensingdevices. For example, the one or more remote sensing devices may recordone or more spectral images, record one or more environmentalparameters, and record imager position, attitude, and time correspondingto the one or more spectral images.

In some implementations, system 10 may be configured to preprocess andcalibrate the one or more spectral images (e.g., process step 1008) byone or more hardware processors configured to execute a machine-readableinstruction component that is the same as or similar to image revisioncomponent 30.

In some implementations, system 10 may calculate a numerical combinationand apply image sharpening (e.g., process steps 1010 and 1012) by one ormore hardware processors configured to execute a machine-readableinstruction component that is the same as or similar to contrastadjustment component 32.

In some implementations, system 10 may set an initial numericalcombination threshold, calculate a number of vegetation detections,adjust the numerical combination threshold, and determine a thresholdvalue where a blob count plateaus (e.g., process steps 1014, 1016, 1018and 1020) by one or more hardware processors configured to execute amachine-readable instruction component that is the same as or similar tobackground clutter segregation component 34.

In some implementations, system 10 may apply erosion to the one or moreimages, segregate crops from non-crops based on size statistics,determine a spatial frequency and an orientation of peak energy (e.g.,process steps 1022, 1024, and 1026) by one or more hardware processorsconfigured to execute a machine-readable instruction component that isthe same as or similar to crop segregation component 36.

In some implementations, system 10 may classify crops and non-crops bycrop row masking, classify crops and non-crops by spectral signature,and calculate a ground area of the one or more spectral images (e.g.,process steps 1028, 1030, and 1032) by one or more hardware processorsconfigured to execute a machine-readable instruction component that isthe same as or similar to crop row attributes component 38.

In some implementations, system 10 may determine crop and non-cropdensities (e.g., process step 1034) by one or more hardware processorsconfigured to execute a machine-readable instruction component that isthe same as or similar to crop density determination component 40.

In some implementations, system 10 may spatially interpolate the cropand non-crop densities onto a geo-grid (e.g., process step 1036) by oneor more hardware processors configured to execute a machine-readableinstruction component that is the same as or similar to presentationcomponent 42.

Although the present technology has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred implementations, it is to be understoodthat such detail is solely for that purpose and that the technology isnot limited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present technology contemplates that, to theextent possible, one or more features of any implementation can becombined with one or more features of any other implementation.

What is claimed is:
 1. A system configured for classifying plants withinan area, the system comprising: non-transitory storage media storingmachine-readable instructions configured to cause one or more hardwareprocessors to: receive output signals provided by one or more remotesensing devices mounted to an unmanned aircraft system, the outputsignals conveying information related to one or more images of a landarea, the one or more images being spatially resolved and including oneor more spectral bands within a target wavelength range; and segregateimage regions corresponding to living vegetation from image regionscorresponding to background clutter, the background clutter includingone or more of soil, rock, liquid, man-made materials, or non-livingvegetation, wherein segregating image regions comprises: classifyingcontent of image pixels as belonging to a vegetation class or tobackground clutter based on an adjustable threshold of spectralreflectance combinations; and classifying groups of contiguousvegetation pixels as plants belonging to one of one or more vegetationclasses responsive to characteristics of a pixel group being proximateto a description of one of one or more of the vegetation classes.
 2. Thesystem of claim 1, wherein the one or more hardware processors arefurther configured by machine-readable instructions to count a number ofplants within a vegetation class.
 3. The system of claim 1, wherein theone or more hardware processors are further configured bymachine-readable instructions to calculate a size of plants within avegetation class.
 4. The system of claim 1, wherein the one or morehardware processors are further configured by machine-readableinstructions to calculate a geo-position of plant centroids of avegetation class within the land area.
 5. The system of claim 1, whereinthe one or more hardware processors are further configured bymachine-readable instructions to assign groups of contiguous pixels to aclass responsive to characteristics of a group being proximate tocharacteristics of a pixel region of interest selected by a user througha graphical user interface.
 6. The system of claim 1, wherein the one ormore hardware processors are further configured by machine-readableinstructions such that the target wavelength range is 400 nanometers to3,000 nanometers.
 7. The system of claim 1, wherein the one or morehardware processors are further configured by machine-readableinstructions to: revise one or more intensity non-uniformities of theone or more images; revise one or more spatial distortions of the one ormore images; revise one or more intensity values for variations in solarirradiance of the one or more images; or register one or more pixelsfrom one or more channels to a common pixel space.
 8. The system ofclaim 1, wherein segregating image regions corresponding to the livingvegetation from image regions corresponding to the background clutterfurther comprises utilizing one or more differing spectral reflectancenumerical combinations across one or more wavelength bands.
 9. Thesystem of claim 8, wherein the one or more hardware processors arefurther configured by machine-readable instructions to amplify one ormore spatial frequency components corresponding to a numericalcombination.
 10. The system of claim 1, wherein the one or more hardwareprocessors are further configured by machine-readable instructions to:determine a spacing of one portion of the living vegetation from one ormore other portions of the living vegetation in pixels; determine thepixel's Ground Sample Dimension using externally provided spacinginformation and the spacing in pixels; and determine an area of landportrayed by the one or more images using the number of pixels in theimage and the pixel's Ground Sample Dimension.
 11. A system configuredfor classifying plants within an area, the system comprising:non-transitory storage media storing machine-readable instructionsconfigured to cause one or more hardware processors to: receive outputsignals provided by one or more remote sensing devices mounted to anunmanned aircraft system, the output signals conveying informationrelated to one or more images of a land area, the one or more imagesbeing spatially resolved and including one or more spectral bands withina target wavelength range; segregate image regions corresponding toliving vegetation from image regions corresponding to backgroundclutter, the background clutter including one or more of soil, rock,liquid, man-made materials, or non-living vegetation, whereinsegregating image regions comprises: classifying content of image pixelsas belonging to a vegetation class or to background clutter based on anadjustable threshold of spectral reflectance combinations; andclassifying groups of contiguous vegetation pixels as plants belongingto one of one or more vegetation classes responsive to characteristicsof a pixel group being proximate to a description of one of one or moreof the vegetation classes; and determine a living vegetation density bydetermining a first area corresponding to the living vegetation anddividing the first area by a total determined area of the land.
 12. Asystem configured for classifying plants within an area, the systemcomprising: non-transitory storage media storing machine-readableinstructions configured to cause one or more hardware processors to:receive output signals provided by one or more remote sensing devicesmounted to an unmanned aircraft system, the output signals conveyinginformation related to one or more images of a land area, the one ormore images being spatially resolved and including one or more spectralbands within a target wavelength range; characterize a referencespectral signature of living vegetation within the land area based onthe output signals; wherein the reference spectral signature of livingvegetation within the land area is determined based on the outputsignals for a user selected region of interest of the land area;statistically compare a spectral signature of each pixel in an image ofthe land area to the reference spectral signature; and assign each pixelas belonging to a same class or another class as the reference spectralsignature.
 13. A method for classifying plants within an area, themethod comprising: receiving, with one or more hardware processors,output signals provided by one or more remote sensing devices mounted toan unmanned aircraft system, the output signals conveying informationrelated to one or more images of a land area, the one or more imagesbeing spatially resolved and including one or more spectral bands withina target wavelength range; and segregating, with the one or morehardware processors, image regions corresponding to living vegetationfrom image regions corresponding to background clutter, the backgroundclutter including one or more of soil, rock, liquid, man-made materials,or non-living vegetation, wherein segregating image regions comprises:classifying content of image pixels as belonging to a vegetation classor to background clutter based on an adjustable threshold of spectralreflectance combinations; and classifying groups of contiguousvegetation pixels as plants belonging to one of one or more vegetationclasses responsive to characteristics of a pixel group being proximateto a description of one of one or more of the vegetation classes. 14.The method of claim 13, further comprising counting, with the one ormore hardware processors, a number of plants within a vegetation class.15. The method of claim 13, further comprising calculating, with the oneor more hardware processors, a size of plants within a vegetation class.16. The method of claim 13, further comprising calculating, with the oneor more hardware processors, a geo-position of plant centroids of avegetation class within the land area.
 17. The method of claim 13,further comprising assigning, with the one or more hardware processors,groups of contiguous pixels to a class responsive to characteristics ofa group being proximate to characteristics of a pixel region of interestselected by a user through a graphical user interface.
 18. The method ofclaim 13, wherein the target wavelength range is 400 nanometers to 3,000nanometers.
 19. The method of claim 13, wherein segregating imageregions corresponding to the living vegetation from image regionscorresponding to the background clutter further comprises utilizing oneor more differing spectral reflectance numerical combinations across oneor more wavelength bands.
 20. A method for classifying plants within anarea, the method comprising: receiving, with one or more hardwareprocessors, output signals provided by one or more remote sensingdevices mounted to an unmanned aircraft system, the output signalsconveying information related to one or more images of a land area, theone or more images being spatially resolved and including one or morespectral bands within a target wavelength range; characterizing, withthe one or more hardware processors, a reference spectral signature ofliving vegetation within the land area based on the output signals; thereference spectral signature being determined based on the outputsignals for a user selection of a region of interest of the land area;statistically comparing, with the one or more hardware processors, aspectral signature of each pixel in an image of the land area to thereference spectral signature; and assigning, with the one or morehardware processors, each pixel as belonging to a same class or anotherclass as the reference spectral signature.